Integrating target capture with whole genome sequencing of recent and natural history collections to explain the phylogeography of wild-growing and cultivated Cannabis
Bibliographic record
Abstract
Cannabis has provided important and versatile services to humans for millennia. Domestication and subsequent dispersal have resulted in various landraces and cultivars. Unravelling the phylogeography of this genus poses considerable challenges due to its complex history. We relied on a Hyb-Seq approach (combining target capture with shotgun sequencing), with the universal Angiosperms353 enrichment panel, to explore the genetic structure of wild-growing accessions and cultivars by implementing phylogenomic and population genomic workflows on the same Hyb-Seq data. Our findings support the treatment of Cannabis as a monotypic genus (C. sativa L.), structured into three main genetic groups—E Asia, Paleotropis, and Boreal—with clear phylogeographic signal despite significant levels of admixture. The E Asia group was sister to the Paleotropis and the Boreal groups. Individuals within the Paleotropis group could be further structured into three subgroups: Iranian Plateau, C & S China and Himalayas, and Indoafrica. Individuals from the Boreal group split into two subgroups: Eurosiberia and W Mongolia and Caucasus and Mediterranean. Hemp and drug-type landraces and cultivars consistently matched their putative geographic origin. These findings enhance our understanding of the genetic patterns in Cannabis and provide a framework for future research into its current and past genetic diversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".